Year: 2025
Type: article
Abstract: The article provides an analytical overview of subspace clustering methods that allow to process high-dimensional data characterized by a large number of features and their values. The methods provide the ability to analyze missing and noisy data. Clustering is performed not in the full feature space, but in its projections, without replacing the original set of features with their linear combinations. This allows reducing the dimensionality of the feature space under consideration while maintaining the ability for the user to interpret the clustering results. The main stages of the clustering process within the considered methods are highlighted and described in detail. Attention is paid to the use of additional user constraints to improve the accuracy of the resulting partitions. The analyzed methods are widely used in various data mining problems, such as image and video recognition, text processing, and genome research.
Source: Transaction Kola Science Centre
Authors: Olga N. Zuenko, Olga V. Fridman
Institution: Kola Science Centre
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7128730375